ACIT4510 Statistical Learning Emneplan

Grade scale A-F.

The participants are expected to know basic concepts in linear algebra, programming and statistics (within the scope and content of e.g. DATA3800 - Introduction to Data Science with Scripting).

Two internal examiners. External examiner is used periodically.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• will have a good understanding the different concepts and methods of supervised and unsupervised statistical learning and how to apply them on large data.
• has advanced knowledge of probabilistic formulation of the various learning problems.
• has focused knowledge of theoretical aspects of the different methods in machine learning and statistical learning, as well as a deep knowledge of concepts and assumptions behind each method.

Skills

Upon successful completion of the course, the student:

• can apply different high-dimensional regression techniques on data
• can apply different classification techniques on data
• can apply clustering techniques on data
• can apply dimension reduction techniques on data
• can make informed decisions on which method suits best for a particular problem and/or data set
• can derive learning algorithms for new models and analyze new data with them.

General competence

Upon successful completion of the course, the student:

• can apply different predictive models on data and assess their performance
• can use supervised and unsupervised learning in different real life problem

Practical experience with deep machine learning. Knowledge of computer graphics and image processing is preferable, but not strictly required.

• Convolutional neural networks in 3D
• Deep learning for point clouds
• Convolutional neural networks on graphs
• Neural radiance fields
• Joint embedding for images and 3D data

An individual project report approximately 2500 - 5000 words, excluding appendixes.

The exam can be appealed,

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Grade scale A-F.

One internal examiner. External examiners are used periodically.

Professor Pedro Lind